Neural Response Threshold Detection via Template Correlation
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Solution Overview
Problem
Conventional techniques for removing noise and stimulus artifact from neural recording signals in neural stimulation systems are often ineffective, making it difficult to determine the neural response threshold current level, especially when noise and artifact signals are present.
Innovation Solution
The method involves identifying neural response signals and non-response signals at various stimulation current levels, analyzing trends between them to automatically determine the neural response threshold current level, using techniques such as principal component analysis and confidence interval calculations to denoise and identify neural responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional filtering techniques are used to remove noise from neural recording signals, then noise removal is attempted, but the technique is ineffective when noise frequency is similar to the neural response frequency
Solution Approach 1:
The patent introduces an intermediary signal processing approach using template correlation. A template signal representing the expected neural response is correlated with the recorded signal to identify and extract the neural response component, separating it from noise and artifact. This intermediary template allows selective extraction of the neural response without being affected by noise at similar frequencies.
Solution Approach 2:
The patent replaces conventional frequency-based filtering (analog/mechanical approach) with digital signal processing techniques including template correlation and adaptive filtering. This substitution allows more sophisticated noise rejection by analyzing signal patterns and temporal characteristics rather than relying solely on frequency separation.
2Measurement precision
If signal correlation techniques are used to remove noise, then noise removal is attempted, but the technique is ineffective when evoked neural responses vary between patients
Solution Approach 1:
The patent implements dynamic adaptation by adjusting the template signal based on individual patient responses. The system starts with a generic template but progressively refines it by incorporating patient-specific response characteristics. This dynamic updating process allows the correlation technique to adapt to inter-patient variability while maintaining effective noise removal.
Solution Approach 2:
The patent employs feedback mechanisms where the system continuously monitors the quality of neural response detection and adjusts the template accordingly. By using the actual patient responses to refine the template, the system creates a feedback loop that improves both the specificity to patient anatomy and the effectiveness of noise removal over time.
3Extent of automation
If automated methods are used to determine neural response threshold, then automation is improved, but accurate identification becomes difficult when noise and artifact signals are present
Solution Approach 1:
The patent uses template correlation as an intermediary mechanism that enables automated identification. The template serves as a mediator between the raw signal and the automated detection algorithm, allowing the system to automatically recognize neural responses by measuring correlation strength against the expected response pattern, thereby achieving both automation and accuracy.
Solution Approach 2:
The patent replaces manual visual inspection and judgment with automated digital signal processing algorithms. By substituting the mechanical process of human analysis with computational correlation techniques, the system achieves automated threshold determination while maintaining high accuracy through mathematical rigor and objective measurement criteria.
4Measurement precision
If multiple neural recording signals are collected at different current levels, then threshold determination is improved, but the complexity of signal analysis increases
Solution Approach 1:
The patent segments the signal analysis process into distinct functional components: template generation, signal collection at multiple current levels, template correlation with each signal, and threshold determination. This segmentation allows each component to be optimized independently and simplifies the overall analysis by breaking down the complex task into manageable steps with clear objectives.
Data Source
AI summary
Methods of automatically determining a neural response threshold current level include identifying one or more neural response signals at one or more corresponding stimulation current levels, identifying one or more non-response signals at one or more corresponding stimulation current levels, and analyzing a trend between the neural response signals and the non-response signals. Systems for automatically determining a neural response threshold current level include one or more devices configured to identify one or more neural response signals at one or more corresponding stimulation current levels, identify one or more non-response signals at one or more corresponding stimulation current levels; and analyze a trend between the neural response signals and the non-response signals.